1. Field of the Invention
The present invention relates to a region extracting method for medical images in which regions of organic parts such as organs including liver and pancreas, blood vessels, tumors, and the like are extracted by image processing from medical images taken by use of radiation diagnosis systems such as CT (computed tomography), MRI (magnetic resonance imaging), nuclear medicine, CR (computed radiography), DSA (digital subtraction angiography), and DR (real-time radiography).
2. Description of the Prior Art
While CT and MRI, for example, have conventionally been known as radiation medical diagnosis systems, the technical progress in these fields has recently made it possible to take high-resolution images in a short time. In the field of CT, for example, with multislice CT coming into practical use, an organ such as liver as a whole can be photographed twice in a single respiration-holding time.
Since it has become possible to attain high-resolution images in a short time as such, there has been an increasing demand for extracting regions of organic parts such as organs and tumors from thus captured images, visualizing them to facilitate their viewing for patients and academic uses in terms of explanation, quantifying them so as to measure their volume, and using them for various purposes such as the making of surgery plans. In many cases, however, radiologists and the like have manually been extracting regions of organic parts from the captured images while seeing the images sheet by sheet. Therefore, in the CT of liver in which hundreds of images are taken at one time, enormous labors are necessary in terms of time and mental efforts to extract the whole liver region.
In view of such a background, studies for automatically extracting predetermined organic regions from medical images by use of machines such as computers have vigorously been carried out in regions such as lung field and brain in particular, from which contours and lesions are relatively easy to extract. However, automatic extractions in regions hard to extract have not yielded sufficient results yet. Known as a method of automatically extracting a liver region from an abdomen-contrasted CT image, for example, are a method comprising the steps of making a histogram of respective concentration values of pixels from data of a single image and extracting the liver region by threshold processing, and a method comprising the steps of carrying out threshold processing by using a partial histogram and approximating a contour with a spline curve.
However, these conventional methods may not be reliable in the detection in areas about the liver such as parts in contact with surrounding tissues such as spleen having a CT value on a par with that of liver, parts having uneven CT values within the same liver region, and parts in which low-concentration lesions exist in the contour area. In particular, their failure to attain the reliability of determining a liver region in areas where the liver and surrounding tissues are in contact with each other has been a large obstacle to practical use.
Therefore, in view of the state where, with multislice CT coming into practical use, the whole liver region can be photographed twice in a single respiration-holding time, whereby respective images in two time phases with different circulating statuses of a contrast medium can be obtained without any positional shift of organs, the inventors have proposed a method of automatically extracting a liver region by using image data of two time phases in which the liver region is captured at substantially the same position (IEICE Technical Report MI99-39 (1999-11), P51–P58).
In the above-mentioned method, from respective concentration values of pixels located at the same position between images in two time phases concerning the same liver region different from each other in terms of time from when the contrast medium is injected until imaging, a two-dimensional histogram having coordinates based on respective concentration values of images is determined, a distribution range of pixel concentration value corresponding to the liver region is estimated from the distribution status of this two-dimensional histogram, and the liver region is extracted according to this estimation.
It has been found out that the above-mentioned method yields results better than those in a conventional method in which the liver region is extracted from data of a single image. Thereafter, some follow-up experiments concerning the above-mentioned method have revealed that the correlation of pixel concentration values in the liver region between different time phases may differ greatly among patients even when the images are taken under similar conditions.